---
title: "UNICEF Global Adolescent Mortality Story"
author: "Prabhjas Singh (16815)"
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# ๐ Executive Summary & Project Details
**A Comprehensive Visualization and Analysis Project**
< br >
**Name:** Prabhjas Singh (16815)
**University:** Dublin City University
**Program:** MSc in Management (Strategy)
**Module:** BAA1030 โ Data Analytics and Storytelling
**Submission Date:** 27 April 2025
< br >
Adolescents represent a pivotal stage of human development. Despite health advances, adolescent mortality remains a global concern.
This report explores adolescent mortality patterns (ages 10โ14 years), evaluating gender disparities, regional inequalities, temporal trends, and links with economic development.
Visualizations and insights aim to highlight global health gaps and support targeted intervention strategies.
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---
```{python}
# Dataset loading
import pandas as pd
from plotnine import *
import plotly.express as px
indicator = pd.read_csv('unicef_indicator_1_cleaned.csv' )
metadata = pd.read_csv('unicef_metadata_cleaned.csv' )
indicator.columns = indicator.columns.str .strip().str .lower()
metadata.columns = metadata.columns.str .strip().str .lower()
data = pd.merge(indicator, metadata, on= 'country' , how= 'left' )
latest_year = data['time_period' ].max ()
```
---
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# ๐ Global Mortality Mapping: Regional Inequalities
**Study Focus:**
Visualize global mortality rates geographically.
**Evaluation Goal:**
Spot regional mortality hotspots where adolescents face extreme risks.
```{python}
data['country' ] = data['country' ].str .title()
latest_global_data = data[data['time_period' ] == latest_year]
fig = px.choropleth(
latest_global_data,
locations= "country" ,
locationmode= "country names" ,
color= "obs_value" ,
color_continuous_scale= [
(0.0 , "rgb(255,245,245)" ),
(0.3 , "rgb(255,200,200)" ),
(0.6 , "rgb(255,100,100)" ),
(1.0 , "rgb(178,34,34)" )
],
title= "Global Distribution of Adolescent Mortality Rates" ,
labels= {'obs_value' : 'Mortality Rate (per 1,000)' }
)
fig.show()
```
> **Insight:**
> Sub-Saharan Africa and South Asia remain mortality epicenters.
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---
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# ๐ Global Gender Differences in Adolescent Mortality (Top 10 Countries)
**Study Focus:**
Analyzing gender gaps globally by highlighting the top 10 countries.
**Evaluation Goal:**
Identify where males face greater mortality challenges.
```{python}
latest_data = data[data['time_period' ] == latest_year]
latest_data = latest_data[latest_data['sex' ].isin(['Male' , 'Female' ])]
top10_countries = latest_data.groupby('country' )['obs_value' ].mean().sort_values(ascending= False ).head(10 ).index.tolist()
top10_gender_data = latest_data[latest_data['country' ].isin(top10_countries)]
(
ggplot(top10_gender_data) +
aes(x= 'country' , y= 'obs_value' , fill= 'sex' ) +
geom_bar(stat= 'identity' , position= 'dodge' ) +
scale_fill_manual(values= {"Male" : "#17a2b8" , "Female" : "#e377c2" }) +
theme_minimal() +
labs(
title= 'Top 10 Countries: Adolescent Mortality by Gender' ,
x= 'Country' ,
y= 'Mortality Rate (per 1,000)' ,
fill= 'Gender'
)
)
```
> **Insight:**
> Males face higher mortality risks globally, dominated by Sub-Saharan Africa.
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---
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# ๐ณ๐ฌ Nigeria Adolescent Mortality Trends: A National Case Study
**Why Nigeria?**
Nigeria has historically struggled with high adolescent mortality.
```{python}
nigeria = data[(data['country' ] == 'Nigeria' ) & (data['time_period' ] <= latest_year)]
(
ggplot(nigeria) +
aes(x= 'time_period' , y= 'obs_value' ) +
geom_line(color= "#17a2b8" , size= 1.8 ) +
geom_point(color= 'black' , size= 3 ) +
theme_minimal() +
labs(
title= 'Nigeria Adolescent Mortality Rate Over Time' ,
x= 'Year' ,
y= 'Mortality Rate (per 1,000)'
)
)
```
> **Insight:**
> Nigeria shows slow but steady improvement, but progress remains below global averages.
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---
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# ๐ต Economic Influence on Adolescent Survival
**Study Focus:**
Analyze the link between GDP and adolescent mortality.
```{python}
bins = [0 , 1000 , 5000 , 10000 , 50000 , 1000000 ]
labels = ['<1k' , '1k-5k' , '5k-10k' , '10k-50k' , '>50k' ]
data['gdp_band' ] = pd.cut(data['gdp per capita (constant 2015 us$)' ], bins= bins, labels= labels)
latest_gender_data = data[(data['time_period' ] == latest_year) & (data['sex' ].isin(['Male' , 'Female' ]))]
avg_band_mortality = latest_gender_data.groupby(['gdp_band' , 'sex' ])['obs_value' ].mean().reset_index()
(
ggplot(avg_band_mortality) +
aes(x= 'gdp_band' , y= 'obs_value' , group= 'sex' , color= 'sex' ) +
geom_line(size= 2 ) +
geom_point(size= 3 ) +
scale_color_manual(values= {"Male" : "#17a2b8" , "Female" : "#e377c2" }) +
theme_minimal() +
labs(
title= 'Average Mortality Rate by GDP Band (Gender Split)' ,
x= 'GDP Band' ,
y= 'Average Mortality Rate (per 1,000)' ,
color= 'Gender'
)
)
```
> **Insight:**
> Mortality drops sharply after $10,000 GDP per capita.
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---
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# ๐งน Conclusion: Addressing Global Adolescent Health Disparities
โ
**Key Findings:**
- Males are at higher mortality risk.
- Regional and economic disparities persist.
- Economic growth helps improve adolescent survival.
โ
**Recommendations:**
- Strengthen health infrastructure.
- Promote gender-sensitive health policies.
- Leverage economic development for youth health equity.
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---
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# ๐ References
- UNICEF Official Website: [ https://www.unicef.org ](https://www.unicef.org)
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